Fuzzy Model Research Articles

Fuzzy Resilient Control of DC Microgrids with Constant Power Loads Based on Markov Jump Models

This paper addresses the fuzzy resilient control of DC microgrids with constant power loads. The DC microgrid is subject to abrupt parameter changes which are described by the Markov jump model. Due to the constant power loads, the DC microgrid exhibits nonlinear dynamics which are characterized by a T-S fuzzy model. According to the parallel distributed compensation principle, mode-dependent fuzzy resilient controllers are designed to stabilize the resultant T-S fuzzy Markov jump DC microgrid. The “resilient” means the controller could cope with the uncertainty caused by the inaccurate execution of the control laws. This uncertainty is governed by a Bernoulli distributed random variable and thus may not occur. Then, the mean square exponential stability is analyzed for the closed-loop system by using the mode-dependent Lyapunov function. Since the stability conditions are not convex, a design algorithm is further derived to calculate the fuzzy resilient controller gains. Finally, simulations are provided to test the effectiveness of the proposed results.

Data-driven planning in socially responsible textile units amidst uncertainty

Social responsibility is a key factor for organizations to achieve sustainable success in the modern competitive market. This study proposes a hybrid VIKOR method to evaluate textile suppliers based on their social performance under uncertain and multi-objective conditions. The method can handle fuzzy, stochastic, and interval data simultaneously. The social criteria for the evaluation are derived from the literature review, the SA8000 standards, and the United Nations’ recommendations. Some of the criteria are also aligned with the World Bank’s Social Responsibility Diamond Model and the United Nations’ Sustainable Development Goals. Moreover, this study presents a fuzzy mathematical model for fabric purchasing that incorporates social criteria and the quality level into the optimization process. A goal programming method is developed based on the mathematical properties of the multi-objective model. A numerical study is conducted in the textile industry to demonstrate the efficiency and effectiveness of the proposed approaches. A comprehensive sensitivity analysis has been performed to investigate the behavior of the presented mathematical model under different conditions, and the results have been discussed concerning the insights for managers and stakeholders in the textile industry. The proposed model demonstrates that: 1) Customer demand and fabric orders have a direct relationship with increasing sales. 2) The fabric unit price has a direct impact on the quality value and requires cost control policies or pricing negotiations with suppliers. 3) Improving supplier and customer relations and formulating pricing consistent with social value are among the most important issues for the success of the textile and clothing industry. The best-fitting line successfully explains the variability of social performance and customer demand with an accuracy of 99.35 %.

Fuzzy Evaluation Model for Operational Performance of Air Cleaning Equipment

Global warming has led to the continuous deterioration of the living environment, in which air quality directly affects human health. In addition, the severity of the COVID-19 pandemic has further increased the attention to indoor air quality. Indoor clean air quality is not only related to human health but also related to the quality of the manufacturing environment of clean rooms for numerous high-tech processes, such as semiconductors and packaging. This paper proposes a comprehensive model for evaluating, analyzing, and improving the operational performance of air cleaning equipment. Firstly, three operational performance evaluation indexes, such as the number of dust particles, the number of colonies, and microorganisms, were established. Secondly, the 100(1− α)% upper confidence limits of these three operational performance evaluation indexes were deduced to construct a fuzzy testing model. Meanwhile, the accumulated value of ϕ was used to derive the evaluation decision-making value. The proposed model can help companies identify the key quality characteristics that need to be improved. Furthermore, the competitiveness of cooperative enterprises towards smart manufacturing can be strengthened, so that enterprises can not only fulfill their social responsibilities while developing the economy but also take into account the sustainable development of enterprises and the environment.

Correction: Intuitionistic fuzzy rough set model based on k-means and its application to enhance prediction of aptamer–protein interacting pairs

Correction: Intuitionistic fuzzy rough set model based on k-means and its application to enhance prediction of aptamer–protein interacting pairs

Stabilization for 2-D switched T–S fuzzy systems under the state-dependent switching

This study focuses on the analysis of stability and stabilization for a class of two-dimensional (2-D) discrete-time switched systems. Owing to the inherent uncertainty and nonlinearity in real-world engineering systems, the Takagi–Sugeno (T–S) fuzzy model is employed to describe the dynamics of the 2-D switched system. The discussion encompasses two primary models for the 2-D discrete-time switched T–S fuzzy system (2DSTSFS), specifically the Roesser model and the Fornasini–Marchesini local state-space model. For 2DSTSFSs, this paper delineates sufficient stability criteria that utilize a state-dependent switching signal, facilitated by the application of the Lyapunov–Metzler inequality, ensuring that state trajectories are globally attracted. Furthermore, the paper articulates sufficient conditions for the stabilization of the 2DSTSFS. Additionally, it elucidates the transformation relationship between the two models. To corroborate the theoretical findings, a practical example is employed, demonstrating the applicability of the proposed theorems.

Two-Warehouse Green Inventory Cloudy Fuzzy Model for Deteriorating Items with Two-Level Trade Credit and Shortages

Two-Warehouse Green Inventory Cloudy Fuzzy Model for Deteriorating Items with Two-Level Trade Credit and Shortages

Creating a fuzzy DEMATEL-ISM-MICMAC -fuzzy BWM model for the organization's sustainable competitive advantage, incorporating green marketing, social responsibility, brand equity and green brand image

This study aimed to create a model that would enable the achievement of a sustainable competitive advantage. The research approach is combining four multi-criteria decision-making tools including Fuzzy DEMATEL, ISM, MICMAC Analysis and Fuzzy BWM to evaluate factors in four key areas included corporate social responsibility, green marketing, brand image, and brand equity in Unilever Corporation. These factors collectively contribute to enhancing competitiveness. The process begins with the fuzzy DEMATEL method, which helps to uncover the cause-and-effect relationships among various components. Following this, the ISM approach is utilized to construct a hierarchical model that clarifies how these components influence and depend on one another. This step is crucial for gaining insights into their collective impact on Unilever's sustainability goals. Next, MICMAC analysis categorizes these factors based on their levels of influence and dependence, pinpointing the most critical elements that drive competitive advantage. Finally, the fuzzy BWM technique is applied to prioritize these key factors, leading to the aimed at bolstering Unilever's long-term competitiveness by fostering a green and sustainable brand image that resonates with consumer values. The key results reveal that the highest priority was given to achieving sustainable development goals. The criterion "Promotion of customer awareness" secured the second position Finally, ensuring the quality of products and services was also recognized as a third key focus area. Identifying these key factors and their interrelationships will provides decision-makers and stakeholders in Unilever company with actionable insights for precise planning and strategic prioritization.

The research on Adaptive Fuzzy Tracking Supervisory Control in the control system of average coolant temperature of lead–bismuth-cooled reactor under multiple operating conditions in the marine environments

Lead–bismuth-cooled reactor nuclear power equipment is severely affected by marine environments, causing operational attitude changes such as heeling and rolling. Conventional controllers cannot track set points rapidly. Therefore, this paper proposes an Adaptive Fuzzy Tracking Supervisory Control method. Firstly, the reactor mathematical model based on fuzzy basis functions is obtained through fuzzy mathematics. Secondly, a coolant average temperature tracking controller is designed on Lyapunov stability theory. To ensure the system returns to stable domain, supervisory compensatory control algorithm is developed. Next, the parameters of fuzzy model are adjusted using Fuzzy universal approximation theorem. By introducing discrepancies between actual system and fuzzy model outputs, parameter adaptive law is designed through Lyapunov theorem. This enables real-time parameter adjustment for fuzzy model and fuzzy tracking supervisory controller, enhancing load tracking performance of coolant’s average temperature. Finally, simulation experiments demonstrate that adaptive fuzzy tracking supervisory controller has stronger adaptability to multiple operating conditions under marine environments.

Parametric modeling of resin-bonded sand mold systems using machine learning-based approaches

This study presents the experimental investigations on modeling of the compression strength and permeability of the resin-bonded sand mold system through Machine Learning approaches. The process of constructing the Fuzzy Logic system was automated by utilizing a data-base and rule-base optimized through genetic algorithms, and the recorded datasets. This research employed three AI models, namely Artificial Neural Networks (ANN), Decision Tree (DT), and Random Forests (RF), using the datasets produced by the GA-tuned Fuzzy model. The objective of this study was to assess and compare the predictive capabilities of three different AI models (Artificial Neural Networks, Decision Tree, and Random Forests) in terms of predicting the values of Compression Strength and Permeability. The complete dataset was divided into two separate subsets, referred to as training data and testing data. Based on the findings, it appears that Random Forest (RF) model exhibits promising potential in accurately predicting the desired mold qualities. The model achieved a high R2 value of 0.9487, indicating a strong correlation with the target values. Additionally, the model demonstrated impressively low Mean Squared Error (MSE) and Mean Absolute Error (MAE) values of 117 and 17.6 points, respectively. Expanding the dataset size may further enhance the efficacy of the models.

Method for Assigning Railway Traffic Managers to Tasks along with Models for Evaluating and Classifying

The occurrence of incidences in railway systems leads to impediments and often delays. Because the railway is an anthropotechnical system, two factors are considered as the source of incidents: technical and human. Minimizing adverse incidents in the railway system is the subject of much discussion and research. One of the areas affecting the performance of railway systems is employees. This article presents a method for assigning railway employees to tasks and models for evaluating and classifying railway employees, consisting of two stages. The first stage involves using a survey method and a fuzzy logic model. Each type of service is assigned feature values, obtaining three parameterized employee-role profiles for the train traffic officer. In the second stage, the participant goes through two of the three available evaluation scenarios, during which errors made during the tasks are counted. Validation results of the proposed approach indicate that the method is 87% effective.

Neighborhood-related q-rung orthopair fuzzy covering-based rough set models and their applications for multi-attribute decision making

Neighborhood-related q-rung orthopair fuzzy covering-based rough set models and their applications for multi-attribute decision making

Fuzzy inventory model with warehouse limits and carbon emission

Fuzzy inventory model with warehouse limits and carbon emission

Cross-Linked Starch as Media for Crystal Violet Elimination from Water: Modeling Batch Adsorption with Fuzzy Regression.

A scalable and cost-effective solution for removing pollutants from water is to use biodegradable and eco-friendly sorbents that are readily available such as starch. The current research explored the removal of crystal violet (CV) dye from water using chemically modified potato starch. The adsorbent was prepared by cross-linking potato starch with sodium trimetaphosphate (STMP). The impact of various operating factors including pH, temperature, contact time, initial CV concentration, and adsorbent dosage on the removal of CV were investigated using batch experiments. The adsorption data were analyzed using a fuzzy regression approach, which provided a range-based representation of the model's output. The cross-linked starch adsorbent was mesoporous, with a mean pore diameter of 9.8 nm and a specific surface area of 2.7 m2/g. The adsorption of CV by the STMP cross-linked potato starch was primarily influenced by the adsorbent dosage, followed by the solution pH, temperature, initial CV concentration, and contact time. The fuzzy regression model accurately predicted the independent experimental data of CV removal with an R2 of 0.985, demonstrating its value as a tool for the continuous monitoring of CV removal as well as optimizing water treatment conditions.

Sustainability Constraints on Rural Road Infrastructure

Research on the sustainability of rural roads is of great significance to the integrated promotion of rural habitat improvement, the maintenance of regional ecological patterns, and the implementation of the rural revitalization strategy. This paper examines the constraints to ensuring the sustainability of road infrastructure in rural Shaanxi, China. Rural road infrastructure plays an important role in poverty alleviation. Rural Shaanxi Province is situated among a variety of terrains and spans a large area. Therefore, using the current situation of rural roads in the province as a starting point, the constraints and their rankings that impede the sustainability of road infrastructure are identified through the fuzzy Delphi method (FDM) and structural equation modeling (SEM), which in turn suggests governance measures that can be taken. The data in this paper come from 498 valid questionnaires obtained from 50 townships and 101 sample villages in Shaanxi Province. Due to the huge number of questionnaires, the questionnaires were tested with the help of SPSS 26.0 software, which showed that the questionnaires had high reliability and validity, and then the structural equation model was validated and modified according to the recommendations of goodness-of-fit statistics measurement and the modification index. Finally, the key factors affecting the sustainable development of rural road infrastructure were finally identified.

A Fuzzy Log-Normal Distribution Model for the Rainfall level of Namakkal District

A Fuzzy Log-Normal Distribution Model for the Rainfall level of Namakkal District

Determining the Suction Capacity of Compacted Clays with Fuzzy-Set Theory

Water suction capacity is an important parameter affecting the swelling properties and volumetric change of soil. Determination of the water suction capacity is made by the experiments which are needed long time in laboratory. However, this has random errors due to the heterogeneous and anisotropic structure of soil sample together with the error caused by the operator made the experiment. Such an estimation problem, included the errors, may be easily solved with the fuzzy-set theory. In this study, the suction capacity of compacted clayey soils is predicted with the fuzzy-set theory. For this reason, the engineering properties of clayey soil (plasticity index, dry density, initial water content and suction capacity) are partitioned into fuzzy subsets, and fuzzy rules are formed. Later, a computer program in the Fortran language is written to estimate the suction capacity of compacted clayey soil from these properties. It is shown that there is a good similarity between the results of the tests and proposed fuzzy logic model.

Tourism Application Development with a Recommendation System based on Fuzzy Logic

The article addresses issues related to the breadth and multifaceted aspects of the tourism industry. It discusses the practical benefits of using recommendation systems based on mobile devices. Mobile applications with recom­mendation systems play an important role in the tourism planning process, offering users personalized suggestions that enhance their travel experience. The main focus of this work is on the use of fuzzy logic as the basis for the rec­ommendation system, which allows for a more flexible and accurate suggestion mechanism that takes into account uncertainties and the diversity of user preferences. The methodology proposed by the authors is based on applying a fuzzy content model to the described problems as an effective extension of the collaborative model. The article describes a method for implementing the recommendation system algorithm, lists the technical tools used to imple­ment the application, presents an activity diagram of the working application, describes the process of searching, preparing, and annotating data used in the working application. The presented software and methodological tools help to solve problems associated with the clear categorization of tourist objects within traditional types of tourism, arising from the broad classification of tourist objects, thereby improving the quality of the tourist experience.

Fuzzy Evaluation Model for Critical Components of Machine Tools

The rapid progression of emerging technologies like the Internet of Things (IoT) and Big Data analytics for manufacturing has driven innovation across various industries worldwide. Production data are utilized to construct a model for quality evaluation and analysis applicable to components processed by machine tools, ensuring process quality for critical components and final product quality for the machine tools. Machine tool parts often encompass several quality characteristics concurrently, categorized into three types: smaller-the-better, larger-the-better, and nominal-the-better. In this paper, an evaluation index for the nominal-the-better quality characteristic was segmented into two single-sided Six Sigma quality indexes. Furthermore, the process quality of the entire component product was assessed by n single-sided Six Sigma quality indexes. According to numerous studies, machine tool manufacturers conventionally base their decisions on small sample sizes (n), considering timeliness and costs. However, this often leads to inconsistent evaluation results due to significant sampling errors. Therefore, this paper established fuzzy testing rules using the confidence intervals of the q single-sided Six Sigma quality indices, serving as the fuzzy quality evaluation model for components of machine tools.

An improved adaptive Kriging method for the possibility-based design optimization and its application to aeroengine turbine disk

Possibility-based design optimization (PBDO) can provide theoretical basis for the structural optimal design of fuzzy uncertainty model in engineering, so as to obtain the optimal design variables. The genetic algorithm (GA) based on adaptive Kriging method for PBDO requires high precision in the whole design space, while the region far from the limit state surface (LSS) of the constraint function has little effect on PBDO, thus greatly affects the computational efficiency. In order to improve the efficiency of PBDO, an improved adaptive Kriging method combined with active possibility constraint (I-AK-AC) is proposed in this paper. In I-AK-AC, the enhanced expected improvement learning function is first put forward to promote the convergence efficiency of Kriging model by reducing the accuracy of the region far from the LSS of the constraint function. Whereafter, the active possibility constraint is identified, and only the boundaries of active possibility constraint need to be approximated precisely by Kriging model. By these ways, the convergence speed of Kriging model is further ameliorated without affecting the computational accuracy, and the efficiency of PBDO is significantly improved. Three test examples and an engineering application of aeroengine turbine disk illustrate the validity and accuracy of the proposed I-AK-AC.

Digital development of manufacturing industry in Yangtze River Delta based on fuzzy control model

Digital development of manufacturing industry in Yangtze River Delta based on fuzzy control model